Strong Predictor-Corrector Euler Methods for Stochastic Differential Equations

Nicola Bruti-Liberati and Eckhard Platen ()

No 222, Research Paper Series from Quantitative Finance Research Centre, University of Technology, Sydney

Abstract: This paper introduces a new class of numerical schemes for the pathwise approximation of solutions of stochastic differential equations (SDEs). The proposed family of strong predictor-corrector Euler methods are designed to handle scenario simulation of solutions of SDEs. It has the potential to overcome some of the numerical instabilities that are often experienced when using the explicit Euler method. This is of importance, for instance, in finance where martingale dynamics arise for solutions of SDEs with multiplicative diffusion coefficients. Numerical experiments demonstrate the improved asymptotic stability properties of the new symmetric predictor-corrector Euler methods.

Keywords: Stochastic differential equations; simulation methods; strong predictor-corrector Euler methods; strong convergence; asymptotic stability (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp and nep-ore
Date: 2008-06-01
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